Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations1416
Missing cells13037
Missing cells (%)29.7%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory343.1 KiB
Average record size in memory248.1 B

Variable types

Text7
DateTime2
Categorical11
Numeric8
Unsupported3

Alerts

Currency has constant value "USD" Constant
Fulfillment Location has constant value "Belly Rubb" Constant
Recipient Region has constant value "CA" Constant
Dataset has 2 (0.1%) duplicate rowsDuplicates
Channels is highly overall correlated with Fulfillment Type and 8 other fieldsHigh correlation
Fulfillment Status is highly overall correlated with Recipient Address and 1 other fieldsHigh correlation
Fulfillment Type is highly overall correlated with Channels and 5 other fieldsHigh correlation
Item Options Total Price is highly overall correlated with Channels and 3 other fieldsHigh correlation
Item Price is highly overall correlated with Channels and 3 other fieldsHigh correlation
Item Quantity is highly overall correlated with Recipient Address and 2 other fieldsHigh correlation
Item Total Price is highly overall correlated with Channels and 3 other fieldsHigh correlation
Order Subtotal is highly overall correlated with Order Tax Total and 4 other fieldsHigh correlation
Order Tax Total is highly overall correlated with Order Subtotal and 4 other fieldsHigh correlation
Order Total is highly overall correlated with Order Subtotal and 4 other fieldsHigh correlation
Recipient Address is highly overall correlated with Channels and 10 other fieldsHigh correlation
Recipient Address 2 is highly overall correlated with Channels and 13 other fieldsHigh correlation
Recipient City is highly overall correlated with Channels and 6 other fieldsHigh correlation
Recipient Country is highly overall correlated with Channels and 8 other fieldsHigh correlation
Recipient Postal Code is highly overall correlated with Channels and 5 other fieldsHigh correlation
Fulfillment Type is highly imbalanced (64.7%) Imbalance
Fulfillment Status is highly imbalanced (97.2%) Imbalance
Recipient Address is highly imbalanced (74.3%) Imbalance
Recipient Country is highly imbalanced (51.8%) Imbalance
Order Shipping Price has 1416 (100.0%) missing values Missing
Order Refunded Amount has 1416 (100.0%) missing values Missing
Fulfillment Notes has 1268 (89.5%) missing values Missing
Recipient Email has 341 (24.1%) missing values Missing
Recipient Address has 1055 (74.5%) missing values Missing
Recipient Address 2 has 1373 (97.0%) missing values Missing
Recipient Postal Code has 1055 (74.5%) missing values Missing
Recipient City has 1055 (74.5%) missing values Missing
Recipient Region has 1055 (74.5%) missing values Missing
Recipient Country has 1013 (71.5%) missing values Missing
Item SKU has 1416 (100.0%) missing values Missing
Item Modifiers has 571 (40.3%) missing values Missing
Item Quantity is highly skewed (γ1 = 34.210835) Skewed
Order Shipping Price is an unsupported type, check if it needs cleaning or further analysis Unsupported
Order Refunded Amount is an unsupported type, check if it needs cleaning or further analysis Unsupported
Item SKU is an unsupported type, check if it needs cleaning or further analysis Unsupported
Item Price has 15 (1.1%) zeros Zeros
Item Options Total Price has 15 (1.1%) zeros Zeros
Item Total Price has 15 (1.1%) zeros Zeros

Reproduction

Analysis started2025-01-02 23:00:53.326301
Analysis finished2025-01-02 23:00:58.958739
Duration5.63 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Order
Text

Distinct262
Distinct (%)18.5%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
2025-01-02T15:00:59.069396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length23
Mean length15.918728
Min length8

Characters and Unicode

Total characters22525
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)4.2%

Sample

1st rowDOORDASH
2nd rowSquare Online 824358568
3rd rowDOORDASH
4th rowDOORDASH
5th rowSquare Online 824358568
ValueCountFrequency (%)
doordash 651
21.3%
square 361
11.8%
online 361
11.8%
delivery 317
10.4%
uber 192
 
6.3%
eats 192
 
6.3%
postmates 149
 
4.9%
payment 41
 
1.3%
link 41
 
1.3%
pickup 24
 
0.8%
Other values (260) 723
23.7%
2025-01-02T15:00:59.311925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1738
 
7.7%
D 1737
 
7.7%
O 1705
 
7.6%
1637
 
7.3%
S 1033
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 759
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10756
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1738
 
7.7%
D 1737
 
7.7%
O 1705
 
7.6%
1637
 
7.3%
S 1033
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 759
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10756
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1738
 
7.7%
D 1737
 
7.7%
O 1705
 
7.6%
1637
 
7.3%
S 1033
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 759
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10756
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1738
 
7.7%
D 1737
 
7.7%
O 1705
 
7.6%
1637
 
7.3%
S 1033
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 759
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10756
47.8%

Order Date
Date

Distinct214
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum2023-12-28 00:00:00
Maximum2024-12-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-02T15:00:59.403893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:59.492296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Currency
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
USD
1416 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4248
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 1416
100.0%

Length

2025-01-02T15:00:59.576752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:00:59.733778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
usd 1416
100.0%

Most occurring characters

ValueCountFrequency (%)
U 1416
33.3%
S 1416
33.3%
D 1416
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1416
33.3%
S 1416
33.3%
D 1416
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1416
33.3%
S 1416
33.3%
D 1416
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1416
33.3%
S 1416
33.3%
D 1416
33.3%

Order Subtotal
Real number (ℝ)

High correlation 

Distinct408
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.569442
Minimum5.64
Maximum1039.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:00:59.803596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.64
5-th percentile19.69
Q135.92
median51.55
Q374.6
95-th percentile172.4075
Maximum1039.95
Range1034.31
Interquartile range (IQR)38.68

Descriptive statistics

Standard deviation80.283926
Coefficient of variation (CV)1.1540114
Kurtosis64.684755
Mean69.569442
Median Absolute Deviation (MAD)17.09
Skewness6.651463
Sum98510.33
Variance6445.5088
MonotonicityNot monotonic
2025-01-02T15:00:59.895241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.15 15
 
1.1%
205.01 12
 
0.8%
19.99 12
 
0.8%
175.44 11
 
0.8%
461.07 10
 
0.7%
57.86 10
 
0.7%
44.27 10
 
0.7%
93.28 10
 
0.7%
58.04 10
 
0.7%
98.22 10
 
0.7%
Other values (398) 1306
92.2%
ValueCountFrequency (%)
5.64 2
0.1%
8.49 1
 
0.1%
8.79 1
 
0.1%
9.24 1
 
0.1%
9.34 1
 
0.1%
9.48 1
 
0.1%
11.19 1
 
0.1%
11.22 1
 
0.1%
11.92 2
0.1%
13.32 4
0.3%
ValueCountFrequency (%)
1039.95 4
 
0.3%
461.07 10
0.7%
450 1
 
0.1%
444.85 2
 
0.1%
358.35 9
0.6%
357.98 2
 
0.1%
318.67 8
0.6%
259.61 1
 
0.1%
211.26 3
 
0.2%
205.01 12
0.8%

Order Shipping Price
Unsupported

Missing  Rejected  Unsupported 

Missing1416
Missing (%)100.0%
Memory size11.2 KiB

Order Tax Total
Real number (ℝ)

High correlation 

Distinct336
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3612571
Minimum0.54
Maximum98.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:00.044480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile1.85
Q13.03
median4.6
Q36.79
95-th percentile16.08
Maximum98.8
Range98.26
Interquartile range (IQR)3.76

Descriptive statistics

Standard deviation7.630332
Coefficient of variation (CV)1.1995007
Kurtosis65.40869
Mean6.3612571
Median Absolute Deviation (MAD)1.71
Skewness6.7104829
Sum9007.54
Variance58.221967
MonotonicityNot monotonic
2025-01-02T15:01:00.140388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.46 19
 
1.3%
9.32 15
 
1.1%
3.32 15
 
1.1%
5.23 13
 
0.9%
8.09 12
 
0.8%
17.86 12
 
0.8%
2.94 12
 
0.8%
16.38 11
 
0.8%
8.86 10
 
0.7%
2.23 10
 
0.7%
Other values (326) 1287
90.9%
ValueCountFrequency (%)
0.54 2
0.1%
0.81 1
0.1%
0.84 1
0.1%
0.88 1
0.1%
0.89 1
0.1%
0.9 1
0.1%
1.06 1
0.1%
1.07 1
0.1%
1.13 2
0.1%
1.19 2
0.1%
ValueCountFrequency (%)
98.8 4
 
0.3%
43.8 10
0.7%
42.75 1
 
0.1%
42.26 2
 
0.1%
34.04 9
0.6%
34.01 2
 
0.1%
30.27 8
0.6%
24.19 1
 
0.1%
20.07 3
 
0.2%
17.86 12
0.8%

Order Total
Real number (ℝ)

High correlation 

Distinct438
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.83589
Minimum6.18
Maximum1158.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:00.225312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.18
5-th percentile21.89
Q138.92
median58.19
Q383.28
95-th percentile190.71
Maximum1158.75
Range1152.57
Interquartile range (IQR)44.36

Descriptive statistics

Standard deviation90.079012
Coefficient of variation (CV)1.1572941
Kurtosis62.791085
Mean77.83589
Median Absolute Deviation (MAD)20.145
Skewness6.5411437
Sum110215.62
Variance8114.2285
MonotonicityNot monotonic
2025-01-02T15:01:00.311522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.47 15
 
1.1%
243.37 12
 
0.8%
191.82 11
 
0.8%
107.08 10
 
0.7%
21.89 10
 
0.7%
504.87 10
 
0.7%
63.08 10
 
0.7%
101.1 10
 
0.7%
48.48 10
 
0.7%
42.95 9
 
0.6%
Other values (428) 1309
92.4%
ValueCountFrequency (%)
6.18 2
0.1%
9.3 1
 
0.1%
9.63 1
 
0.1%
10.12 1
 
0.1%
10.23 1
 
0.1%
10.38 1
 
0.1%
12.25 1
 
0.1%
13.41 1
 
0.1%
14.59 4
0.3%
15.71 1
 
0.1%
ValueCountFrequency (%)
1158.75 4
 
0.3%
512.75 1
 
0.1%
504.87 10
0.7%
487.11 2
 
0.1%
427.79 2
 
0.1%
412.01 9
0.6%
363.94 8
0.6%
283.8 1
 
0.1%
252.46 3
 
0.2%
243.37 12
0.8%

Order Refunded Amount
Unsupported

Missing  Rejected  Unsupported 

Missing1416
Missing (%)100.0%
Memory size11.2 KiB

Fulfillment Date
Date

Distinct515
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum2023-12-28 13:30:00
Maximum2024-12-20 13:25:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-02T15:01:00.403336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:01:00.491282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Fulfillment Type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Pickup
1248 
Curbside
 
79
Delivery
 
47
Other
 
42

Length

Max length8
Median length6
Mean length6.1483051
Min length5

Characters and Unicode

Total characters8706
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPickup
2nd rowPickup
3rd rowPickup
4th rowPickup
5th rowPickup

Common Values

ValueCountFrequency (%)
Pickup 1248
88.1%
Curbside 79
 
5.6%
Delivery 47
 
3.3%
Other 42
 
3.0%

Length

2025-01-02T15:01:00.577821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:00.652108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pickup 1248
88.1%
curbside 79
 
5.6%
delivery 47
 
3.3%
other 42
 
3.0%

Most occurring characters

ValueCountFrequency (%)
i 1374
15.8%
u 1327
15.2%
P 1248
14.3%
c 1248
14.3%
k 1248
14.3%
p 1248
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1374
15.8%
u 1327
15.2%
P 1248
14.3%
c 1248
14.3%
k 1248
14.3%
p 1248
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1374
15.8%
u 1327
15.2%
P 1248
14.3%
c 1248
14.3%
k 1248
14.3%
p 1248
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1374
15.8%
u 1327
15.2%
P 1248
14.3%
c 1248
14.3%
k 1248
14.3%
p 1248
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Fulfillment Status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Completed
1412 
Canceled
 
4

Length

Max length9
Median length9
Mean length8.9971751
Min length8

Characters and Unicode

Total characters12740
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompleted
2nd rowCompleted
3rd rowCompleted
4th rowCompleted
5th rowCompleted

Common Values

ValueCountFrequency (%)
Completed 1412
99.7%
Canceled 4
 
0.3%

Length

2025-01-02T15:01:00.725781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:00.791307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
completed 1412
99.7%
canceled 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 2832
22.2%
l 1416
11.1%
C 1416
11.1%
d 1416
11.1%
o 1412
11.1%
m 1412
11.1%
p 1412
11.1%
t 1412
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2832
22.2%
l 1416
11.1%
C 1416
11.1%
d 1416
11.1%
o 1412
11.1%
m 1412
11.1%
p 1412
11.1%
t 1412
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2832
22.2%
l 1416
11.1%
C 1416
11.1%
d 1416
11.1%
o 1412
11.1%
m 1412
11.1%
p 1412
11.1%
t 1412
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2832
22.2%
l 1416
11.1%
C 1416
11.1%
d 1416
11.1%
o 1412
11.1%
m 1412
11.1%
p 1412
11.1%
t 1412
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Channels
Categorical

High correlation 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
DoorDash
672 
BELLY RUBB | BBQ Catering | Barbecue To Go and Delivery
361 
Postmates Delivery
341 
Payment Links
 
41
Belly Rubb
 
1

Length

Max length55
Median length18
Mean length22.536723
Min length8

Characters and Unicode

Total characters31912
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowDoorDash
2nd rowBELLY RUBB | BBQ Catering | Barbecue To Go and Delivery
3rd rowDoorDash
4th rowDoorDash
5th rowBELLY RUBB | BBQ Catering | Barbecue To Go and Delivery

Common Values

ValueCountFrequency (%)
DoorDash 672
47.5%
BELLY RUBB | BBQ Catering | Barbecue To Go and Delivery 361
25.5%
Postmates Delivery 341
24.1%
Payment Links 41
 
2.9%
Belly Rubb 1
 
0.1%

Length

2025-01-02T15:01:00.863115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:00.932392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
722
13.3%
delivery 702
13.0%
doordash 672
12.4%
belly 362
6.7%
rubb 362
6.7%
bbq 361
6.7%
catering 361
6.7%
barbecue 361
6.7%
go 361
6.7%
to 361
6.7%
Other values (4) 784
14.5%

Most occurring characters

ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2407
 
7.5%
B 2167
 
6.8%
a 2137
 
6.7%
r 2096
 
6.6%
D 2046
 
6.4%
s 1395
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10613
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2407
 
7.5%
B 2167
 
6.8%
a 2137
 
6.7%
r 2096
 
6.6%
D 2046
 
6.4%
s 1395
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10613
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2407
 
7.5%
B 2167
 
6.8%
a 2137
 
6.7%
r 2096
 
6.6%
D 2046
 
6.4%
s 1395
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10613
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2407
 
7.5%
B 2167
 
6.8%
a 2137
 
6.7%
r 2096
 
6.6%
D 2046
 
6.4%
s 1395
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10613
33.3%

Fulfillment Location
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Belly Rubb
1416 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters14160
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBelly Rubb
2nd rowBelly Rubb
3rd rowBelly Rubb
4th rowBelly Rubb
5th rowBelly Rubb

Common Values

ValueCountFrequency (%)
Belly Rubb 1416
100.0%

Length

2025-01-02T15:01:01.013291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:01.073979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
belly 1416
50.0%
rubb 1416
50.0%

Most occurring characters

ValueCountFrequency (%)
l 2832
20.0%
b 2832
20.0%
e 1416
10.0%
B 1416
10.0%
y 1416
10.0%
1416
10.0%
R 1416
10.0%
u 1416
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2832
20.0%
b 2832
20.0%
e 1416
10.0%
B 1416
10.0%
y 1416
10.0%
1416
10.0%
R 1416
10.0%
u 1416
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2832
20.0%
b 2832
20.0%
e 1416
10.0%
B 1416
10.0%
y 1416
10.0%
1416
10.0%
R 1416
10.0%
u 1416
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2832
20.0%
b 2832
20.0%
e 1416
10.0%
B 1416
10.0%
y 1416
10.0%
1416
10.0%
R 1416
10.0%
u 1416
10.0%

Fulfillment Notes
Text

Missing 

Distinct53
Distinct (%)35.8%
Missing1268
Missing (%)89.5%
Memory size11.2 KiB
2025-01-02T15:01:01.199593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length396
Median length183
Mean length112.08784
Min length12

Characters and Unicode

Total characters16589
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)9.5%

Sample

1st rowDelivery By Doordash. Courier pickup time: 1/17/2024 2:43pm - 2:53pm. For issues, contact 1(855) 599-7066 and provide the following delivery ID: 2150180224. Total Fees Applied: $28.64
2nd rowDelivery By Doordash. Courier pickup time: 1/17/2024 2:43pm - 2:53pm. For issues, contact 1(855) 599-7066 and provide the following delivery ID: 2150180224. Total Fees Applied: $28.64
3rd rowDelivery By Doordash. Courier pickup time: 1/17/2024 2:43pm - 2:53pm. For issues, contact 1(855) 599-7066 and provide the following delivery ID: 2150180224. Total Fees Applied: $28.64
4th rowCURBSIDE PICKUP - DETAILS: Black Hyundai Elantra
5th rowCURBSIDE PICKUP - DETAILS: Black Hyundai Elantra
ValueCountFrequency (%)
143
 
5.5%
pickup 128
 
4.9%
delivery 116
 
4.5%
the 101
 
3.9%
curbside 79
 
3.0%
and 76
 
2.9%
details 70
 
2.7%
for 56
 
2.2%
issues 52
 
2.0%
contact 51
 
2.0%
Other values (252) 1728
66.5%
2025-01-02T15:01:01.437591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2372
 
14.3%
e 1091
 
6.6%
o 681
 
4.1%
i 679
 
4.1%
a 634
 
3.8%
r 591
 
3.6%
t 555
 
3.3%
s 525
 
3.2%
l 517
 
3.1%
n 441
 
2.7%
Other values (65) 8503
51.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2372
 
14.3%
e 1091
 
6.6%
o 681
 
4.1%
i 679
 
4.1%
a 634
 
3.8%
r 591
 
3.6%
t 555
 
3.3%
s 525
 
3.2%
l 517
 
3.1%
n 441
 
2.7%
Other values (65) 8503
51.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2372
 
14.3%
e 1091
 
6.6%
o 681
 
4.1%
i 679
 
4.1%
a 634
 
3.8%
r 591
 
3.6%
t 555
 
3.3%
s 525
 
3.2%
l 517
 
3.1%
n 441
 
2.7%
Other values (65) 8503
51.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2372
 
14.3%
e 1091
 
6.6%
o 681
 
4.1%
i 679
 
4.1%
a 634
 
3.8%
r 591
 
3.6%
t 555
 
3.3%
s 525
 
3.2%
l 517
 
3.1%
n 441
 
2.7%
Other values (65) 8503
51.3%

Recipient Name
Text

Distinct372
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:01.570083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length18
Mean length11.149718
Min length4

Characters and Unicode

Total characters15788
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)5.1%

Sample

1st rowTest T
2nd rowNarek Ekmekjyan
3rd rowPickUp-Narek E
4th rowPickUp-Narek E
5th rowNarek Ekmekjyan
ValueCountFrequency (%)
s 127
 
4.5%
d 71
 
2.5%
k 71
 
2.5%
b 71
 
2.5%
c 68
 
2.4%
a 66
 
2.3%
m 66
 
2.3%
o 65
 
2.3%
g 55
 
1.9%
t 54
 
1.9%
Other values (441) 2134
74.9%
2025-01-02T15:01:01.795953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1432
 
9.1%
a 1339
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 736
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7720
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1432
 
9.1%
a 1339
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 736
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7720
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1432
 
9.1%
a 1339
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 736
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7720
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1432
 
9.1%
a 1339
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 736
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7720
48.9%

Recipient Email
Text

Missing 

Distinct98
Distinct (%)9.1%
Missing341
Missing (%)24.1%
Memory size11.2 KiB
2025-01-02T15:01:01.884536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length38
Mean length31.966512
Min length13

Characters and Unicode

Total characters34364
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)2.0%

Sample

1st rowpoint-of-sale-integration@doordash.com
2nd rownarek.ek@gmail.com
3rd rowpoint-of-sale-integration@doordash.com
4th rowpoint-of-sale-integration@doordash.com
5th rownarek.ek@gmail.com
ValueCountFrequency (%)
point-of-sale-integration@doordash.com 672
62.5%
alex.anthony.diaz@gmail.com 31
 
2.9%
steven.trella@gmail.com 28
 
2.6%
mogshut@gmail.com 28
 
2.6%
bidium@gmail.com 22
 
2.0%
monalapides@gmail.com 12
 
1.1%
m.m.keshishyan@gmail.com 11
 
1.0%
kennysanchez5122@yahoo.com 10
 
0.9%
strwbrytiff@yahoo.com 10
 
0.9%
narumol2003@yahoo.com 8
 
0.7%
Other values (88) 243
 
22.6%
2025-01-02T15:01:02.049596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4795
14.0%
a 2898
 
8.4%
i 2617
 
7.6%
n 2406
 
7.0%
t 2298
 
6.7%
- 2016
 
5.9%
e 1795
 
5.2%
r 1624
 
4.7%
s 1556
 
4.5%
m 1555
 
4.5%
Other values (45) 10804
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4795
14.0%
a 2898
 
8.4%
i 2617
 
7.6%
n 2406
 
7.0%
t 2298
 
6.7%
- 2016
 
5.9%
e 1795
 
5.2%
r 1624
 
4.7%
s 1556
 
4.5%
m 1555
 
4.5%
Other values (45) 10804
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4795
14.0%
a 2898
 
8.4%
i 2617
 
7.6%
n 2406
 
7.0%
t 2298
 
6.7%
- 2016
 
5.9%
e 1795
 
5.2%
r 1624
 
4.7%
s 1556
 
4.5%
m 1555
 
4.5%
Other values (45) 10804
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4795
14.0%
a 2898
 
8.4%
i 2617
 
7.6%
n 2406
 
7.0%
t 2298
 
6.7%
- 2016
 
5.9%
e 1795
 
5.2%
r 1624
 
4.7%
s 1556
 
4.5%
m 1555
 
4.5%
Other values (45) 10804
31.4%

Recipient Phone
Text

Distinct96
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:02.166765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.824153
Min length10

Characters and Unicode

Total characters16743
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.3%

Sample

1st row8559731040
2nd row+13106632447
3rd row8559731040
4th row8559731040
5th row+13106632447
ValueCountFrequency (%)
1 365
20.5%
8552228111 359
20.2%
312-766-6835 341
19.1%
8559731040 313
17.6%
16268647315 35
 
2.0%
16266164211 31
 
1.7%
18184862439 28
 
1.6%
818-822-5060 22
 
1.2%
18186360644 12
 
0.7%
17472562597 11
 
0.6%
Other values (87) 264
14.8%
2025-01-02T15:01:02.373569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3010
18.0%
5 2010
12.0%
8 2002
12.0%
2 1883
11.2%
6 1657
9.9%
3 1322
7.9%
7 947
 
5.7%
0 866
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1572
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3010
18.0%
5 2010
12.0%
8 2002
12.0%
2 1883
11.2%
6 1657
9.9%
3 1322
7.9%
7 947
 
5.7%
0 866
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1572
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3010
18.0%
5 2010
12.0%
8 2002
12.0%
2 1883
11.2%
6 1657
9.9%
3 1322
7.9%
7 947
 
5.7%
0 866
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1572
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3010
18.0%
5 2010
12.0%
8 2002
12.0%
2 1883
11.2%
6 1657
9.9%
3 1322
7.9%
7 947
 
5.7%
0 866
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1572
9.4%

Recipient Address
Categorical

High correlation  Imbalance  Missing 

Distinct16
Distinct (%)4.4%
Missing1055
Missing (%)74.5%
Memory size11.2 KiB
13346 Saticoy St
314 
6615 Sepulveda Boulevard
 
9
15447 Tupper Street
 
5
15825 Saticoy Street
 
5
6333 Coldwater Canyon Avenue
 
4
Other values (11)
 
24

Length

Max length28
Median length16
Mean length16.759003
Min length16

Characters and Unicode

Total characters6050
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.1%

Sample

1st row13346 Saticoy St
2nd row13346 Saticoy St
3rd row13346 Saticoy St
4th row13346 Saticoy St
5th row13346 Saticoy St

Common Values

ValueCountFrequency (%)
13346 Saticoy St 314
 
22.2%
6615 Sepulveda Boulevard 9
 
0.6%
15447 Tupper Street 5
 
0.4%
15825 Saticoy Street 5
 
0.4%
6333 Coldwater Canyon Avenue 4
 
0.3%
5447 Matilija Avenue 4
 
0.3%
7636 Fulton Avenue 3
 
0.2%
13244 Arminta Street 3
 
0.2%
6230 Van Nuys Boulevard 3
 
0.2%
12320 Burbank Boulevard 3
 
0.2%
Other values (6) 8
 
0.6%
(Missing) 1055
74.5%

Length

2025-01-02T15:01:02.469574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saticoy 319
29.2%
13346 314
28.8%
st 314
28.8%
boulevard 18
 
1.6%
avenue 15
 
1.4%
street 14
 
1.3%
6615 9
 
0.8%
sepulveda 9
 
0.8%
tupper 5
 
0.5%
15825 5
 
0.5%
Other values (26) 70
 
6.4%

Most occurring characters

ValueCountFrequency (%)
731
12.1%
t 678
11.2%
S 656
10.8%
3 655
10.8%
a 375
 
6.2%
o 352
 
5.8%
6 347
 
5.7%
1 344
 
5.7%
4 339
 
5.6%
i 332
 
5.5%
Other values (35) 1241
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
731
12.1%
t 678
11.2%
S 656
10.8%
3 655
10.8%
a 375
 
6.2%
o 352
 
5.8%
6 347
 
5.7%
1 344
 
5.7%
4 339
 
5.6%
i 332
 
5.5%
Other values (35) 1241
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
731
12.1%
t 678
11.2%
S 656
10.8%
3 655
10.8%
a 375
 
6.2%
o 352
 
5.8%
6 347
 
5.7%
1 344
 
5.7%
4 339
 
5.6%
i 332
 
5.5%
Other values (35) 1241
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
731
12.1%
t 678
11.2%
S 656
10.8%
3 655
10.8%
a 375
 
6.2%
o 352
 
5.8%
6 347
 
5.7%
1 344
 
5.7%
4 339
 
5.6%
i 332
 
5.5%
Other values (35) 1241
20.5%

Recipient Address 2
Categorical

High correlation  Missing 

Distinct8
Distinct (%)18.6%
Missing1373
Missing (%)97.0%
Memory size11.2 KiB
unit 1
17 
104
9 
25
5 
APT 12 GATE CODE "#1333"
4 
Apt 314
3 
Other values (3)
5 

Length

Max length24
Median length14
Mean length6.5581395
Min length2

Characters and Unicode

Total characters282
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.7%

Sample

1st rowunit 1
2nd rowunit 1
3rd rowunit 1
4th rowunit 1
5th rowunit 1

Common Values

ValueCountFrequency (%)
unit 1 17
 
1.2%
104 9
 
0.6%
25 5
 
0.4%
APT 12 GATE CODE "#1333" 4
 
0.3%
Apt 314 3
 
0.2%
123 3
 
0.2%
Leasing office 1
 
0.1%
214 1
 
0.1%
(Missing) 1373
97.0%

Length

2025-01-02T15:01:02.648226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:02.721483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unit 17
21.2%
1 17
21.2%
104 9
11.2%
apt 7
8.8%
25 5
 
6.2%
12 4
 
5.0%
gate 4
 
5.0%
code 4
 
5.0%
1333 4
 
5.0%
314 3
 
3.8%
Other values (4) 6
 
7.5%

Most occurring characters

ValueCountFrequency (%)
1 41
14.5%
37
13.1%
t 20
 
7.1%
i 19
 
6.7%
n 18
 
6.4%
3 18
 
6.4%
u 17
 
6.0%
4 13
 
4.6%
2 13
 
4.6%
A 11
 
3.9%
Other values (20) 75
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 41
14.5%
37
13.1%
t 20
 
7.1%
i 19
 
6.7%
n 18
 
6.4%
3 18
 
6.4%
u 17
 
6.0%
4 13
 
4.6%
2 13
 
4.6%
A 11
 
3.9%
Other values (20) 75
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 41
14.5%
37
13.1%
t 20
 
7.1%
i 19
 
6.7%
n 18
 
6.4%
3 18
 
6.4%
u 17
 
6.0%
4 13
 
4.6%
2 13
 
4.6%
A 11
 
3.9%
Other values (20) 75
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 41
14.5%
37
13.1%
t 20
 
7.1%
i 19
 
6.7%
n 18
 
6.4%
3 18
 
6.4%
u 17
 
6.0%
4 13
 
4.6%
2 13
 
4.6%
A 11
 
3.9%
Other values (20) 75
26.6%

Recipient Postal Code
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)3.0%
Missing1055
Missing (%)74.5%
Infinite0
Infinite (%)0.0%
Mean91586.657
Minimum91331
Maximum91607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:02.796117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum91331
5-th percentile91406
Q191605
median91605
Q391605
95-th percentile91605
Maximum91607
Range276
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.58019
Coefficient of variation (CV)0.00066145214
Kurtosis7.8284101
Mean91586.657
Median Absolute Deviation (MAD)0
Skewness-3.0786172
Sum33062783
Variance3669.9595
MonotonicityNot monotonic
2025-01-02T15:01:02.865927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
91605 320
 
22.6%
91411 9
 
0.6%
91401 7
 
0.5%
91606 6
 
0.4%
91406 5
 
0.4%
91343 5
 
0.4%
91607 3
 
0.2%
91331 2
 
0.1%
91405 2
 
0.1%
91402 1
 
0.1%
(Missing) 1055
74.5%
ValueCountFrequency (%)
91331 2
 
0.1%
91343 5
 
0.4%
91401 7
 
0.5%
91402 1
 
0.1%
91405 2
 
0.1%
91406 5
 
0.4%
91411 9
 
0.6%
91601 1
 
0.1%
91605 320
22.6%
91606 6
 
0.4%
ValueCountFrequency (%)
91607 3
 
0.2%
91606 6
 
0.4%
91605 320
22.6%
91601 1
 
0.1%
91411 9
 
0.6%
91406 5
 
0.4%
91405 2
 
0.1%
91402 1
 
0.1%
91401 7
 
0.5%
91343 5
 
0.4%

Recipient City
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.6%
Missing1055
Missing (%)74.5%
Memory size11.2 KiB
North Hollywood
297 
Los Angeles
64 

Length

Max length15
Median length15
Mean length14.290859
Min length11

Characters and Unicode

Total characters5159
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLos Angeles
2nd rowLos Angeles
3rd rowLos Angeles
4th rowLos Angeles
5th rowLos Angeles

Common Values

ValueCountFrequency (%)
North Hollywood 297
 
21.0%
Los Angeles 64
 
4.5%
(Missing) 1055
74.5%

Length

2025-01-02T15:01:02.947001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:03.017867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
north 297
41.1%
hollywood 297
41.1%
los 64
 
8.9%
angeles 64
 
8.9%

Most occurring characters

ValueCountFrequency (%)
o 1252
24.3%
l 658
12.8%
361
 
7.0%
N 297
 
5.8%
r 297
 
5.8%
h 297
 
5.8%
t 297
 
5.8%
H 297
 
5.8%
y 297
 
5.8%
w 297
 
5.8%
Other values (7) 809
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1252
24.3%
l 658
12.8%
361
 
7.0%
N 297
 
5.8%
r 297
 
5.8%
h 297
 
5.8%
t 297
 
5.8%
H 297
 
5.8%
y 297
 
5.8%
w 297
 
5.8%
Other values (7) 809
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1252
24.3%
l 658
12.8%
361
 
7.0%
N 297
 
5.8%
r 297
 
5.8%
h 297
 
5.8%
t 297
 
5.8%
H 297
 
5.8%
y 297
 
5.8%
w 297
 
5.8%
Other values (7) 809
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1252
24.3%
l 658
12.8%
361
 
7.0%
N 297
 
5.8%
r 297
 
5.8%
h 297
 
5.8%
t 297
 
5.8%
H 297
 
5.8%
y 297
 
5.8%
w 297
 
5.8%
Other values (7) 809
15.7%

Recipient Region
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing1055
Missing (%)74.5%
Memory size11.2 KiB
CA
361 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters722
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA 361
 
25.5%
(Missing) 1055
74.5%

Length

2025-01-02T15:01:03.094029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:03.154407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ca 361
100.0%

Most occurring characters

ValueCountFrequency (%)
C 361
50.0%
A 361
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 361
50.0%
A 361
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 361
50.0%
A 361
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 361
50.0%
A 361
50.0%

Recipient Country
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing1013
Missing (%)71.5%
Memory size11.2 KiB
US
361 
ZZ
42 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters806
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 361
 
25.5%
ZZ 42
 
3.0%
(Missing) 1013
71.5%

Length

2025-01-02T15:01:03.216173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T15:01:03.277497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
us 361
89.6%
zz 42
 
10.4%

Most occurring characters

ValueCountFrequency (%)
U 361
44.8%
S 361
44.8%
Z 84
 
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 361
44.8%
S 361
44.8%
Z 84
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 361
44.8%
S 361
44.8%
Z 84
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 361
44.8%
S 361
44.8%
Z 84
 
10.4%

Item Quantity
Real number (ℝ)

High correlation  Skewed 

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2740113
Minimum1
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:03.343146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum130
Range129
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5391484
Coefficient of variation (CV)2.7779568
Kurtosis1239.6114
Mean1.2740113
Median Absolute Deviation (MAD)0
Skewness34.210835
Sum1804
Variance12.525571
MonotonicityNot monotonic
2025-01-02T15:01:03.414928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1280
90.4%
2 96
 
6.8%
3 13
 
0.9%
4 13
 
0.9%
5 4
 
0.3%
15 2
 
0.1%
6 2
 
0.1%
7 1
 
0.1%
130 1
 
0.1%
9 1
 
0.1%
Other values (3) 3
 
0.2%
ValueCountFrequency (%)
1 1280
90.4%
2 96
 
6.8%
3 13
 
0.9%
4 13
 
0.9%
5 4
 
0.3%
6 2
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
130 1
 
0.1%
15 2
 
0.1%
14 1
 
0.1%
11 1
 
0.1%
9 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
6 2
 
0.1%
5 4
 
0.3%
4 13
0.9%

Item Name
Text

Distinct75
Distinct (%)5.3%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
2025-01-02T15:01:03.537365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length25
Mean length17.625442
Min length7

Characters and Unicode

Total characters24940
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.8%

Sample

1st rowCRINKLE FRIES
2nd rowGET YOUR BABY BACK!
3rd rowGET YOUR BABY BACK!
4th rowBELLY SLIDERS
5th rowBELLY SLIDERS
ValueCountFrequency (%)
back 289
 
6.7%
baby 282
 
6.5%
ribs 222
 
5.1%
glazed 166
 
3.8%
pork 166
 
3.8%
belly 166
 
3.8%
fries 150
 
3.5%
combo 144
 
3.3%
rib 140
 
3.2%
beef 121
 
2.8%
Other values (100) 2478
57.3%
2025-01-02T15:01:03.749733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2909
 
11.7%
E 2286
 
9.2%
B 1942
 
7.8%
A 1881
 
7.5%
S 1507
 
6.0%
R 1428
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 987
 
4.0%
Other values (55) 8046
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2909
 
11.7%
E 2286
 
9.2%
B 1942
 
7.8%
A 1881
 
7.5%
S 1507
 
6.0%
R 1428
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 987
 
4.0%
Other values (55) 8046
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2909
 
11.7%
E 2286
 
9.2%
B 1942
 
7.8%
A 1881
 
7.5%
S 1507
 
6.0%
R 1428
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 987
 
4.0%
Other values (55) 8046
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2909
 
11.7%
E 2286
 
9.2%
B 1942
 
7.8%
A 1881
 
7.5%
S 1507
 
6.0%
R 1428
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 987
 
4.0%
Other values (55) 8046
32.3%

Item SKU
Unsupported

Missing  Rejected  Unsupported 

Missing1416
Missing (%)100.0%
Memory size11.2 KiB

Item Variation
Categorical

Distinct25
Distinct (%)1.8%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
Regular
729 
Full Rack
129 
Side
116 
Full
103 
4 Bites
80 
Other values (20)
258 

Length

Max length39
Median length7
Mean length6.9130742
Min length4

Characters and Unicode

Total characters9782
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowFull, Truffle salt
2nd rowHalf rack
3rd rowFull Rack
4th row4 sliders
5th row2 sliders

Common Values

ValueCountFrequency (%)
Regular 729
51.5%
Full Rack 129
 
9.1%
Side 116
 
8.2%
Full 103
 
7.3%
4 Bites 80
 
5.6%
Half rack 53
 
3.7%
6 pcs 53
 
3.7%
8 pcs 26
 
1.8%
12 pcs 25
 
1.8%
2 sliders 23
 
1.6%
Other values (15) 78
 
5.5%

Length

2025-01-02T15:01:03.848506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
regular 729
38.2%
full 239
 
12.5%
rack 182
 
9.5%
side 130
 
6.8%
pcs 122
 
6.4%
bites 101
 
5.3%
4 90
 
4.7%
6 60
 
3.1%
half 55
 
2.9%
sliders 35
 
1.8%
Other values (13) 163
 
8.6%

Most occurring characters

ValueCountFrequency (%)
l 1322
13.5%
e 1051
10.7%
a 994
10.2%
u 980
10.0%
R 871
8.9%
r 857
8.8%
g 729
 
7.5%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1322
13.5%
e 1051
10.7%
a 994
10.2%
u 980
10.0%
R 871
8.9%
r 857
8.8%
g 729
 
7.5%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1322
13.5%
e 1051
10.7%
a 994
10.2%
u 980
10.0%
R 871
8.9%
r 857
8.8%
g 729
 
7.5%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1322
13.5%
e 1051
10.7%
a 994
10.2%
u 980
10.0%
R 871
8.9%
r 857
8.8%
g 729
 
7.5%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Item Modifiers
Text

Missing 

Distinct397
Distinct (%)47.0%
Missing571
Missing (%)40.3%
Memory size11.2 KiB
2025-01-02T15:01:03.967391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length236
Median length148
Mean length56.405917
Min length11

Characters and Unicode

Total characters47663
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique304 ?
Unique (%)36.0%

Sample

1st row1 x Blue Cheese
2nd row1 x Pickled peppers, 1 x Signature BBQ Sauce, 1 x Sweet and Spicy BBQ Sauce
3rd row1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce, 1 x Pickled peppers
4th row1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce
5th row1 x Signature BBQ Sauce
ValueCountFrequency (%)
1 1951
19.2%
x 1951
19.2%
bbq 524
 
5.2%
glaze 459
 
4.5%
signature 424
 
4.2%
sauce 294
 
2.9%
dip 287
 
2.8%
pepper 262
 
2.6%
no 179
 
1.8%
salt 143
 
1.4%
Other values (112) 3680
36.2%
2025-01-02T15:01:04.196973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Item Price
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.261045
Minimum0
Maximum450
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:04.286982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q15.64
median9.34
Q323.99
95-th percentile48.99
Maximum450
Range450
Interquartile range (IQR)18.35

Descriptive statistics

Standard deviation23.424241
Coefficient of variation (CV)1.3570581
Kurtosis109.42169
Mean17.261045
Median Absolute Deviation (MAD)7.12
Skewness7.9913359
Sum24441.64
Variance548.69507
MonotonicityNot monotonic
2025-01-02T15:01:04.376657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.93 129
 
9.1%
8.49 127
 
9.0%
3.99 81
 
5.7%
0.99 65
 
4.6%
27.49 64
 
4.5%
23.99 53
 
3.7%
7.25 48
 
3.4%
48.99 48
 
3.4%
6.99 43
 
3.0%
9.34 42
 
3.0%
Other values (70) 716
50.6%
ValueCountFrequency (%)
0 15
 
1.1%
0.5 5
 
0.4%
0.85 6
 
0.4%
0.95 17
 
1.2%
0.99 65
4.6%
1.45 14
 
1.0%
1.5 1
 
0.1%
1.99 36
2.5%
2.49 4
 
0.3%
2.64 3
 
0.2%
ValueCountFrequency (%)
450 1
 
0.1%
259.61 1
 
0.1%
257 2
 
0.1%
162 3
 
0.2%
127 2
 
0.1%
117 8
0.6%
100 1
 
0.1%
67.99 1
 
0.1%
60 1
 
0.1%
57.99 3
 
0.2%

Item Options Total Price
Real number (ℝ)

High correlation  Zeros 

Distinct235
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.856589
Minimum0
Maximum450
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:04.466916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q16.29
median11.35
Q323.99
95-th percentile48.99
Maximum450
Range450
Interquartile range (IQR)17.7

Descriptive statistics

Standard deviation23.51773
Coefficient of variation (CV)1.3170337
Kurtosis107.35822
Mean17.856589
Median Absolute Deviation (MAD)7.36
Skewness7.891606
Sum25284.93
Variance553.08362
MonotonicityNot monotonic
2025-01-02T15:01:04.550258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.49 96
 
6.8%
3.99 81
 
5.7%
0.99 65
 
4.6%
34.93 64
 
4.5%
48.99 48
 
3.4%
27.49 46
 
3.2%
9.34 40
 
2.8%
1.99 36
 
2.5%
23.99 34
 
2.4%
4.99 32
 
2.3%
Other values (225) 874
61.7%
ValueCountFrequency (%)
0 15
 
1.1%
0.5 5
 
0.4%
0.85 6
 
0.4%
0.95 17
 
1.2%
0.99 65
4.6%
1.45 14
 
1.0%
1.5 1
 
0.1%
1.99 36
2.5%
2.49 4
 
0.3%
2.64 3
 
0.2%
ValueCountFrequency (%)
450 1
 
0.1%
260 1
 
0.1%
259.61 1
 
0.1%
257 1
 
0.1%
162 3
0.2%
127 2
 
0.1%
120 3
0.2%
117 5
0.4%
100 1
 
0.1%
67.99 1
 
0.1%

Item Total Price
Real number (ℝ)

High correlation  Zeros 

Distinct602
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.972938
Minimum0
Maximum567.98
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-01-02T15:01:04.633583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.08
Q17.65
median13.315
Q327.35
95-th percentile53.65
Maximum567.98
Range567.98
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation33.727937
Coefficient of variation (CV)1.5349762
Kurtosis102.00157
Mean21.972938
Median Absolute Deviation (MAD)8.925
Skewness8.2927105
Sum31113.68
Variance1137.5737
MonotonicityNot monotonic
2025-01-02T15:01:04.717342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37 48
 
3.4%
1.08 36
 
2.5%
9.3 35
 
2.5%
10.23 33
 
2.3%
38.25 31
 
2.2%
26.27 21
 
1.5%
7.94 18
 
1.3%
53.64 17
 
1.2%
19.11 17
 
1.2%
25.68 16
 
1.1%
Other values (592) 1144
80.8%
ValueCountFrequency (%)
0 15
1.1%
0.54 2
 
0.1%
0.55 3
 
0.2%
0.91 2
 
0.1%
0.93 2
 
0.1%
0.97 1
 
0.1%
0.99 2
 
0.1%
1.02 3
 
0.2%
1.03 3
 
0.2%
1.04 7
0.5%
ValueCountFrequency (%)
567.98 1
0.1%
492.75 1
0.1%
438 1
0.1%
284.7 1
0.1%
283.8 1
0.1%
281.41 1
0.1%
267.74 1
0.1%
229.49 1
0.1%
177.39 2
0.1%
176.92 1
0.1%

Interactions

2025-01-02T15:00:57.844422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.076756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.672326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.190812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.710509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.203355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.739313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.237463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.913900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.148792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.736052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.254164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.774901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.267292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.799855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.299170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.977535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.211904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.799371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.319368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.842801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.341263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.866388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.450401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:58.044453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.279261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.870764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.390881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.900822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.406641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.930540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.512285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:58.108900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.352802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.931557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.449425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.958251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.471673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.989303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.571645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:58.175937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.484188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.996130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.516814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.021164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.538172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.054920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.642546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:58.235788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.541356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.057329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.581362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.085571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.605249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.117782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.702766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:58.298419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:54.609578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.126450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:55.646244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.146410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:56.665150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.176125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T15:00:57.763051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-02T15:01:04.783376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ChannelsFulfillment StatusFulfillment TypeItem Options Total PriceItem PriceItem QuantityItem Total PriceItem VariationOrder SubtotalOrder Tax TotalOrder TotalRecipient AddressRecipient Address 2Recipient CityRecipient CountryRecipient Postal Code
Channels1.0000.0740.6530.5010.5010.1570.5260.1490.4290.4290.4341.0001.0001.0000.9991.000
Fulfillment Status0.0741.0000.2840.0000.0000.0000.0000.0000.0000.0000.0000.9800.9240.1860.0000.000
Fulfillment Type0.6530.2841.0000.1090.1090.1560.2250.1190.4720.4720.4750.6790.9240.8350.9980.557
Item Options Total Price0.5010.0000.1091.0000.987-0.1400.9630.0670.0650.0740.0700.4450.7040.1050.1390.023
Item Price0.5010.0000.1090.9871.000-0.1330.9540.0670.0690.0780.0730.4450.7040.1050.1390.045
Item Quantity0.1570.0000.156-0.140-0.1331.0000.0690.0000.2290.2280.2291.0001.0001.0000.2440.005
Item Total Price0.5260.0000.2250.9630.9540.0691.0000.0000.1200.1320.1240.4370.9240.1230.3710.027
Item Variation0.1490.0000.1190.0670.0670.0000.0001.0000.0370.0370.0480.0000.0000.1250.2140.205
Order Subtotal0.4290.0000.4720.0650.0690.2290.1200.0371.0000.9890.9960.7201.0000.1570.828-0.005
Order Tax Total0.4290.0000.4720.0740.0780.2280.1320.0370.9891.0000.9890.7201.0000.1570.828-0.005
Order Total0.4340.0000.4750.0700.0730.2290.1240.0480.9960.9891.0000.5861.0000.1670.827-0.012
Recipient Address1.0000.9800.6790.4450.4451.0000.4370.0000.7200.7200.5861.0001.0000.8091.0000.982
Recipient Address 21.0000.9240.9240.7040.7041.0000.9240.0001.0001.0001.0001.0001.0001.0001.0000.924
Recipient City1.0000.1860.8350.1050.1051.0000.1230.1250.1570.1570.1670.8091.0001.0001.0000.657
Recipient Country0.9990.0000.9980.1390.1390.2440.3710.2140.8280.8280.8271.0001.0001.0001.0001.000
Recipient Postal Code1.0000.0000.5570.0230.0450.0050.0270.205-0.005-0.005-0.0120.9820.9240.6571.0001.000

Missing values

2025-01-02T15:00:58.420462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-02T15:00:58.678557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-02T15:00:58.858076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OrderOrder DateCurrencyOrder SubtotalOrder Shipping PriceOrder Tax TotalOrder TotalOrder Refunded AmountFulfillment DateFulfillment TypeFulfillment StatusChannelsFulfillment LocationFulfillment NotesRecipient NameRecipient EmailRecipient PhoneRecipient AddressRecipient Address 2Recipient Postal CodeRecipient CityRecipient RegionRecipient CountryItem QuantityItem NameItem SKUItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price
0DOORDASH2023/12/28USD9.34NaN0.8910.23NaN12/28/2023, 1:30 PMPickupCompletedDoorDashBelly RubbNaNTest Tpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1CRINKLE FRIESNaNFull, Truffle salt1 x Blue Cheese8.499.3410.23
1Square Online 8243585682023/12/30USD36.17NaN3.4445.04NaN12/30/2023, 4:15 PMPickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryBelly RubbNaNNarek Ekmekjyannarek.ek@gmail.com+1310663244713346 Saticoy Stunit 191605.0Los AngelesCAUS1GET YOUR BABY BACK!NaNHalf rack1 x Pickled peppers, 1 x Signature BBQ Sauce, 1 x Sweet and Spicy BBQ Sauce23.9926.1928.68
2DOORDASH2023/12/30USD53.40NaN4.3157.71NaN12/30/2023, 3:50 PMPickupCompletedDoorDashBelly RubbNaNPickUp-Narek Epoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GET YOUR BABY BACK!NaNFull Rack1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce, 1 x Pickled peppers34.9337.1340.13
3DOORDASH2023/12/30USD53.40NaN4.3157.71NaN12/30/2023, 3:50 PMPickupCompletedDoorDashBelly RubbNaNPickUp-Narek Epoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1BELLY SLIDERSNaN4 sliders1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce14.5716.2717.58
4Square Online 8243585682023/12/30USD36.17NaN3.4445.04NaN12/30/2023, 4:15 PMPickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryBelly RubbNaNNarek Ekmekjyannarek.ek@gmail.com+1310663244713346 Saticoy Stunit 191605.0Los AngelesCAUS1BELLY SLIDERSNaN2 slidersNaN9.989.9810.93
5DOORDASH2024/01/03USD24.84NaN2.3627.20NaN01/03/2024, 2:29 PMPickupCompletedDoorDashBelly RubbNaNAlan Spoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GET YOUR BABY BACK!NaNHalf rack1 x Signature BBQ Sauce23.9924.8427.20
6DOORDASH2024/01/04USD35.37NaN2.6037.97NaN01/04/2024, 5:31 PMPickupCompletedDoorDashBelly RubbNaNBenjamin Bpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GET YOUR BABY BACK!NaNHalf rack1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce23.9925.6927.58
7DOORDASH2024/01/04USD35.37NaN2.6037.97NaN01/04/2024, 5:31 PMPickupCompletedDoorDashBelly RubbNaNBenjamin Bpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1CRINKLE FRIESNaNSide, Rosemary pepper1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce4.996.697.18
8DOORDASH2024/01/04USD35.37NaN2.6037.97NaN01/04/2024, 5:31 PMPickupCompletedDoorDashBelly RubbNaNBenjamin Bpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GRILLED SWEET CORNNaNRegularNaN2.992.993.21
9DOORDASH2024/01/05USD20.81NaN1.9822.79NaN01/05/2024, 1:49 PMPickupCompletedDoorDashBelly RubbNaNTimothy Lpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1BELLY SLIDERSNaN2 sliders1 x Signature BBQ Sauce9.9810.8311.86
OrderOrder DateCurrencyOrder SubtotalOrder Shipping PriceOrder Tax TotalOrder TotalOrder Refunded AmountFulfillment DateFulfillment TypeFulfillment StatusChannelsFulfillment LocationFulfillment NotesRecipient NameRecipient EmailRecipient PhoneRecipient AddressRecipient Address 2Recipient Postal CodeRecipient CityRecipient RegionRecipient CountryItem QuantityItem NameItem SKUItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price
1406Uber Eats Delivery 8F8192024/12/18USD37.90NaN3.4641.36NaN12/18/2024, 1:58 PMPickupCompletedPostmates DeliveryBelly RubbNaN8F819-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1CRISPY CHICKEN SANDWICHNaNRegularNaN16.4616.4618.02
1407Postmates Delivery EAA452024/12/19USD22.06NaN1.2023.26NaN12/19/2024, 11:26 AMPickupCompletedPostmates DeliveryBelly RubbNaNEAA45-Anthony S.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1ARTISAN MAC AND CHEESENaNFullNaN12.6012.6013.80
1408Postmates Delivery EAA452024/12/19USD22.06NaN1.2023.26NaN12/19/2024, 11:26 AMPickupCompletedPostmates DeliveryBelly RubbNaNEAA45-Anthony S.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1PINEAPPLE SLAWNaNFullNaN9.469.469.46
1409Square Online 18865889552024/12/19USD63.34NaN6.0275.69NaN12/19/2024, 12:55 PMPickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryBelly RubbNaNRip Geru- Maariplawlavc@gmail.com+1725577516613346 Saticoy StNaN91605.0North HollywoodCAUS1ARTISAN MAC AND CHEESENaNFull1 x Add gorgonzola! Make it special.12.6014.3515.71
1410Square Online 18865889552024/12/19USD63.34NaN6.0275.69NaN12/19/2024, 12:55 PMPickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryBelly RubbNaNRip Geru- Maariplawlavc@gmail.com+1725577516613346 Saticoy StNaN91605.0North HollywoodCAUS1BEEF BACK RIBS (Full Rack)NaNRegular1 x Sweet&Spicy Glaze, 1 x Please, cut it!48.9948.9953.65
1411Uber Eats Delivery 1B73A2024/12/19USD32.19NaN3.0635.25NaN12/19/2024, 2:59 PMPickupCompletedPostmates DeliveryBelly RubbNaN1B73A-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1LOADED FRIESNaNRegular1 x Blue Cheese Sauce Drizzle, 1 x Salt and Pepper11.9912.7413.95
1412Uber Eats Delivery 1B73A2024/12/19USD32.19NaN3.0635.25NaN12/19/2024, 2:59 PMPickupCompletedPostmates DeliveryBelly RubbNaN1B73A-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1CIABATTA STEAK SANDWICHNaNRegularNaN19.4519.4521.30
1413Uber Eats Delivery 86B6C2024/12/19USD40.12NaN3.8143.93NaN12/19/2024, 6:40 PMPickupCompletedPostmates DeliveryBelly RubbNaN86B6C-Martin T.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1CHICKEN WINGSNaN6 pcs1 x Sweet&Spicy glaze (Pairs well w/ LemonPeeper seasoning), 1 x Zesty Lemon Pepper17.9417.9419.64
1414Uber Eats Delivery 86B6C2024/12/19USD40.12NaN3.8143.93NaN12/19/2024, 6:40 PMPickupCompletedPostmates DeliveryBelly RubbNaN86B6C-Martin T.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1LOADED FRIESNaNRegular1 x No Cheddar, 1 x No Provolone, 1 x Signature BBQ Sauce Drizzle, 1 x Sweet and Spicy BBQ Sauce Dip, 1 x Lemon Pepper, 1 x Add Pork Rib Meat (Of-The-Bone!)11.9922.1824.29
1415Uber Eats Delivery A97C32024/12/20USD48.99NaN4.6553.64NaN12/20/2024, 1:25 PMPickupCompletedPostmates DeliveryBelly RubbNaNA97C3-Olga G.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1BEEF BACK RIBS (Full Rack)NaNRegular1 x Signature BBQ Glaze48.9948.9953.64

Duplicate rows

Most frequently occurring

OrderOrder DateCurrencyOrder SubtotalOrder Tax TotalOrder TotalFulfillment DateFulfillment TypeFulfillment StatusChannelsFulfillment LocationFulfillment NotesRecipient NameRecipient EmailRecipient PhoneRecipient AddressRecipient Address 2Recipient Postal CodeRecipient CityRecipient RegionRecipient CountryItem QuantityItem NameItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price# duplicates
0DOORDASH2024/05/18USD76.056.7582.805/18/2024, 9:04 PMPickupCompletedDoorDashBelly RubbNaNDavid Cpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1BAKED BABY POTATOESRegularNaN6.296.296.852
1DOORDASH2024/05/18USD76.056.7582.805/18/2024, 9:04 PMPickupCompletedDoorDashBelly RubbNaNDavid Cpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1Beef Short RibRegularNaN27.4927.4929.932